دانلود رایگان مقاله مقایسه جایگزین ها برای عدم همبستگی نامتعارف در مدل های بازاریابی مستقیم

Abstract

In customer relationship management (CRM), it is critical for managers to understand how and when customers terminate their relationships with the company in order to make more accurate predictions for CLV. However, in many non-contractual settings, customer churn is not easily observed, which presents difficulty for estimating customer retention. In this research, we present a framework for estimating multichannel customer relationship dynamics in a non-contractual setting that flexibly allows for relationship revival and investigates the effects of different channel experiences and marketing communication on retention and profitability. We use a multi-segment, multivariate hidden Markov modeling framework to model three managerially relevant customer behaviors: purchase amount, purchase incidence, and channel choice. Using data from a multichannel clothing retailer, we uncover two latent relationship states that customers migrate to and from — an active state and an inactive state characterized by different levels of purchase frequency, responsiveness to marketing, and profitability. We find that an offline (retail-store) channel can be used to migrate customers from an inactive state to an active state, effectively serving the purpose of “education” or “revival,” whereas an online channel is most effective in keeping the existing active customers active, thus serving the purpose of “retention”. Using counterfactual analysis, we highlight an opportunity for the multichannel firm to optimize marketing strategies to dynamically manage and increase the retention and hence also the value of its customer base

Conclusions and Directions for Future Research

We have proposed a framework to estimate multichannel customer retention in a non-contractual setting. The proposed multivariate HMM simultaneously models the changes in purchase incidence, channel choice, and purchase amount with respect to relationship states and investigates the impact of marketing communications on state transition. The model advances prior research on customer retention in non-contractual service settings by 1) modeling retention probabilities as driven by channel experiences and marketing, 2) modeling multiple decisions of high managerial interests, 3) incorporating channel preference evolution, and 4) flexibly allowing customer “revival” from inactive to active states